Sr. Principal Data Scientist – Market Access

Eli Lilly and Company • Us, IN

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About The Position

Lilly USA Technology at Lilly Market Access is seeking a Senior Principal Data Scientist to design and build agentic AI and machine learning solutions for employer-intelligence and real-world-evidence initiatives. The role focuses on solving complex, high-volume business problems with intelligent, autonomous, measurable AI systems that can scale across multiple use cases.

The position analyzes complex data, develops predictive and generative models, and builds agentic AI systems. The successful candidate owns the AI reasoning layer end to end, from problem framing and multi-agent workflow design through model development and evaluation. The role works closely with business and Technology at Lilly partners to understand needs, develop hypotheses, test them rigorously, and create reusable capabilities rather than one-off solutions. It also provides technical leadership, establishes standards, mentors team members, and manages multiple projects.

Lilly IT applies technology to advance the company's purpose of creating medicines that improve lives. The Lilly USA Technology at Lilly team partners with market access colleagues who work with payers, employers, and government customers to improve affordability and access. The team uses real-world evidence and health outcomes data to reduce friction in the patient journey, improve outcomes, and deliver business results. Travel is 0–10%.

Requirements

• PhD in Computer Science, Data Science, Statistics, Mathematics, or a related quantitative or technical field and at least 4 years of experience developing, deploying, or maintaining AI, machine learning, or data systems in production; OR a master's degree in a listed or related field and at least 6 years of such experience; OR a bachelor's degree in a listed or related field and at least 8 years of such experience.
• Foundational knowledge in communication, leadership, teamwork, problem solving, solution and blueprint definition, business acumen, architectural processes, technical standards, project delivery, and the industry.
• Ability to engage with business partners, elicit and define requirements, translate business needs into technical solutions, and communicate effectively with technical and non-technical stakeholders.
• Ability to analyze large, complex data domains and develop practical, measurable AI solutions.
• Ability to work with geographically dispersed business and technical colleagues.

Nice To Haves

• A track record of delivering LLM or agentic AI solutions into production with measurable business results.
• Strong foundation in machine learning algorithms and statistical analysis.
• Proficiency in Python.
• Technical leadership experience, including setting technical direction for AI or ML projects, mentoring data scientists or engineers, or defining model-development and evaluation standards.
• Experience designing agent-to-agent orchestration and multi-agent workflows with modern agentic frameworks such as LangGraph, LangChain, or equivalents, including tool use and autonomous reasoning loops.
• Experience building evaluation and benchmarking approaches for LLM and agentic systems, including groundedness, hallucination, and reliability measurement, and integrating evaluation into development lifecycles.
• Experience with prompt engineering, retrieval-augmented generation, embeddings, vector databases, knowledge graphs, and fine-tuning or distillation of small, specialized models.
• Experience with supervised and unsupervised learning, anomaly detection, NLP, time-series, and uncertainty quantification.
• Experience with AWS or Azure, Databricks, SQL and NoSQL data stores, deep learning frameworks, common data-manipulation libraries, Git, and CI/CD.
• Previous experience in healthcare, pharma, payer or employer, or real-world evidence.

Responsibilities

• Analyze complex data and develop predictive and generative models for employer and real-world-evidence business problems.
• Design and build agentic AI systems, including agent-to-agent orchestration, multi-agent workflows, tool and environment use, and autonomous reasoning loops.
• Own the AI reasoning layer end to end, from framing problems and designing workflows to building models and evaluation frameworks.
• Build rigorous AI evaluation frameworks covering groundedness, hallucination measurement, benchmark design, regression testing, confidence and uncertainty estimation, and human-in-the-loop review.
• Apply supervised and unsupervised learning, anomaly detection, natural language processing, time-series methods, and statistical inference.
• Develop, fine-tune, and distill models, including specialized models trained on proprietary data, with appropriate governance and data controls.
• Apply prompt engineering, retrieval-augmented generation, embeddings, vector search, and knowledge-graph methods to ground AI reasoning in trusted, cited sources.
• Design reusable, horizontal AI capabilities for use across multiple downstream experiences and teams.
• Ensure data quality, reproducibility, and auditability across models and agentic workflows in a regulated environment.
• Translate business problems into solvable AI problems and communicate insights and recommendations to technical and non-technical stakeholders.
• Conduct research and stay current with developments in agentic AI, evaluation, and model training.
• Set technical direction for agentic AI work, mentor team members, and define evaluation and model-development standards.
• Manage and prioritize multiple projects simultaneously.
• Engage with business partners, identify opportunities to deliver value, and translate business requirements and processes into technical terms and vice versa.
• Elicit and define requirements, ensure appropriate business roles participate in solution execution, and take part in design reviews to ensure requirements traceability.
• Network with Technology at Lilly colleagues to identify solutions, reuse existing processes and services, and influence technology strategy.

Benefits

• Company bonus, depending in part on company and individual performance.
• Eligibility to participate in a company-sponsored 401(k).
• Pension.
• Vacation benefits.
• Eligibility for medical, dental, vision, and prescription drug benefits.
• Flexible benefits, including healthcare and/or dependent day care flexible spending accounts.
• Life insurance and death benefits.
• Certain time-off and leave-of-absence benefits.
• Well-being benefits, including an employee assistance program, fitness benefits, and employee clubs and activities.

What This Job Offers

• Job Type: Full-time
• Career Level: Principal